Description

Book Synopsis

Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included.

Features

  • Exclusive information on machine learning and data analytics applications with respect to civil engineering

  • Includes many machi

    Table of Contents

    1. Introduction 2. Artificial Neural Networks 3. Fuzzy Logic 4. Support Vector Machine 5. Genetic Algorithm (GA) 6. Hybrid Systems 7. Data Statistics and Analytics 8. Applications in the Civil Engineering Domain 9. Conclusion and Future Scope of Work

A Primer on Machine Learning Applications in

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Order before 4pm tomorrow for delivery by Tue 27 Jan 2026.

A Hardback by Paresh Chandra Deka

15 in stock


    View other formats and editions of A Primer on Machine Learning Applications in by Paresh Chandra Deka

    Publisher: Taylor & Francis Ltd
    Publication Date: 1/7/2019 12:11:00 AM
    ISBN13: 9781138323391, 978-1138323391
    ISBN10: 113832339X
    Also in:
    Algorithms and data structures Artificial intelligence (AI) Neural networks and fuzzy systems

    Description

    Book Synopsis

    Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included.

    Features

    • Exclusive information on machine learning and data analytics applications with respect to civil engineering

    • Includes many machi

      Table of Contents

      1. Introduction 2. Artificial Neural Networks 3. Fuzzy Logic 4. Support Vector Machine 5. Genetic Algorithm (GA) 6. Hybrid Systems 7. Data Statistics and Analytics 8. Applications in the Civil Engineering Domain 9. Conclusion and Future Scope of Work

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